Intelligent Design 3.0

The Scientific Renaissance of ID Research

In the Scopes era, religious fundamentalists tried to ban evolution. In the subsequent decades, Darwinian fundamentalists worked hard trying to ban intelligent design (ID). But one thing that has quietly grown over the years is a community of research scientists who simply want to know: Is there evidence for design in nature?

The most popular strategy for suppressing the case for design is to claim, “It’s not science!” For many critics, it boils down to two claims: ID doesn’t use the methods of science, and ID has no research program. As scientists working at Discovery Institute, the leading ID think tank, we know these claims are false because we use ID on a regular basis to guide research and to manage a vibrant community of scientists who are conducting scientific research to explore the evidence for design.

What is ID?

If we’re going to argue that ID is science, we should first define the term.

Intelligent design is a scientific theory which uses the methods of historical sciences to infer that many features of nature are best explained by an intelligent cause rather than an (apparently) undirected cause like natural selection—because those features exhibit forms of information and complexity which in our experience only come from intelligence. First and foremost, ID is a scientific theory of design detection, but it’s also a scientific theory that can be applied to guide research investigating how the natural world operates.

As a theory of design detection, ID uses information as a reliable indicator of whether an object or natural feature was intelligently designed. We call this complex and specified information (CSI).

Roughly speaking, something is complex if it is unlikely. But this alone is not enough to infer design. To understand why, imagine you visit Mount Rainier. You don’t infer design simply because Mount Rainier has a unique, complex, and unlikely shape. Why? Because unlikely things can occur naturally.

But then you visit Mount Rushmore. This mountain also has an unlikely shape, but it’s also specified to match a pattern—the faces of four presidents. From experience, we know this combination of complexity and specification happens only by design.

Science Does History

Crucial to detecting design are the methods of historical sciences, which study present-day causes and apply them to explain the past. Based upon present-day experience, we observe that intelligent agents alone generate high CSI, and that natural mechanisms such as random mutation and natural selection cannot generate high CSI. This means that when we find high CSI in a natural object, we can make a “design inference.”

ID thus seeks to find in nature the kind of information—CSI—that we know from experience is produced only by intelligent agents. By studying the actions of human intelligent agents and the properties of known designed objects, we can construct positive, testable predictions about what we should find if design is present in nature.

A Snowball of ID Research

The outline above sketched out how we can use methods of science to detect design. Some critics will then complain that ID just says, “Goddidit,” or “IDdidit,” and quits. Yet ID is far more potent than they appreciate.

Having established evidence for design in nature, we can then model and predict what natural systems should look like if they were designed—effectively applying design-detection criteria to discover how natural systems should function and operate in the present.

This sketch also hints at two basic types of ID research:

• Pure Design Research: Investigates whether design is the best explanation for the origin of natural features.

• Applied Design Research: Applies design reasoning to better understand how natural features work in the present day.

ID research has basically progressed in three phases, which we like to compare to a snowball.

During ID 1.0 (1985–1999), the snowball was small but witnessed the development of core ID concepts like CSI and irreducible complexity as hallmark indicators of design.

Then came ID 2.0 (2000–2015). The snowball picked up mass as researchers began testing fundamental ID questions like protein evolvability, i.e., whether natural mechanisms can produce CSI, and strengthened the theoretical groundwork for detecting design through making “inferences to the best explanation.”

Since 2016, we’ve been in the phase we call ID 3.0 (2016–present), where the groundwork is laid, the predictions are made, and ID is being applied to make new scientific discoveries that were unexpected on an evolutionary paradigm. Over 100 peer-reviewed, ID-inspired research papers have been published during this time. The snowball has rapidly grown and picked up speed to the point where we’re struggling to keep up with the mad rate at which new scientists are joining our community! That’s our problem, not yours—so let’s review some examples of Pure Design and Applied Design research.

— Pure Design Research —

Protein Origins: Protein scientist Douglas Axe published the results of mutational-sensitivity experiments on the beta-lactamase enzyme which showed that only 1 in 1077 amino acid sequences would yield a functional domain (i.e., an independently folded region) in a typical protein.¹ This result demonstrates high CSI in proteins.

Later, Axe, along with biologists Ann Gauger and Marci Reeves, published multiple experimental research papers showing that modifying one protein so that it could perform the function of a highly similar protein would require more mutations than could possibly arise over the entire history of the earth.² Their results further confirmed high CSI in proteins.

Orphan Genes: According to evolution, genes evolve from other similar genes. Yet as more genomes are sequenced, it’s become apparent that many genes are unique—as much as 10 to 30 percent of a genome for many organisms might consist of these “orphan genes.”³ Orphan genes were predicted by ID because they show informational discontinuity, something evolution can’t handle. ID researchers are studying orphan genes and creating improved tools for identifying them.⁴

Systematics: Evolutionists claim that species are related through universal common descent in a “tree of life.” ID allows for common ancestry, but also considers common design, where similarities result from a designer re-using components in different species. Computer scientist Winston Ewert has developed a “dependency graph” model of organismal relationships based upon his observation that programmers often re-use coding modules.

Ewert compared the distribution of various gene families and found they fit a common design-based “dependency graph” 103000 times better than a Darwinian tree!⁵ He then studied echolocation, a complex trait found in bats and whales. Common descent can’t explain why this trait is shared by distantly related species, so evolutionists resort to “convergent evolution.” But Ewert has shown that a common-design-based dependency graph is superior for explaining the unexpected distribution of echolocation traits among highly disparate mammals.⁶

Evolutionary Waiting Times: This project is evaluating whether the geological record allows enough time to evolve mutations required for new complex biological features. This is a direct test of whether standard evolutionary mechanisms can produce the information in life—or whether design is implicated. The team has published two papers developing a mathematical model⁷ and is currently studying the evolvability of whales. Their results demonstrate that the maximum time available for major transitions (according to the fossil record) is far too short for an undirected process to generate the required new information. Only a mind could create the information so quickly.

— Applied Design Research —

Junk DNA: Evolution has long predicted that our genomes are filled with useless “junk DNA.” But because we observe that intelligent agents make things for a purpose, ID proponents have long predicted that “junk DNA” is functional. It’s now known that ID was right! Biologist Richard Sternberg was an early vanguard critic of junk DNA, publishing a 2002 paper in Annals of the New York Academy of Sciences.⁸ Jonathan Wells’s 2011 book The Myth of Junk DNA continued this trend. Today the ID community boasts multiple scientists investigating functions for junk DNA, and we’ve launched a “Junk DNA” workgroup to foster research collaborations on this topic!

Brain Blood Flow: Your heart pulses blood through your brain, but if these pulses aren’t carefully controlled, blood flow would burst the brain’s fragile capillaries. Applying the assumption that the brain is a “designed system,” Dr. Michael Egnor, a professor of neurosurgery at Stony Brook University, has sought to understand how our physiology allows blood to flow smoothly into and through the brain.

By carefully measuring blood flow, his team has found that brain capillary blood flow is controlled by a band stop filter, and cerebrospinal fluid flow is controlled by a band pass filter—features known from electrical engineering. This reveals intelligent design of the brain. They have published papers in BIO-Complexity⁹ and the Journal of Neurosurgery Pediatrics,¹⁰ and Egnor anticipates his ID-inspired model could lead to improved treatments of brain bleeds!

Glycolysis Efficiency: Glycolysis is a universal metabolic pathway that performs vital functions such as producing energy or helping to synthesize life’s building blocks. Led by biochemist Emily Reeves, this project published a paper applying “reverse systems engineering” to understand why glycolysis works the way it does:

Biological systems exhibit features that are traditionally associated with good top-down requirements-driven system engineering practices, such as modularity, optimality, robustness, common protocols, and design reuse.

Although “the near-uniformity of central metabolism across life has traditionally been attributed solely to universal common descent,” they found that “from a systems engineering perspective” this “uniformity might be expected” due to its functional elegance: Glycolysis pathways “maximize thermodynamic efficiency” and use “recyclable waste products” which “simplify maintenance of ecosystem homeostasis.”¹¹ The structured sequence of steps in glycolysis reveals a meticulously prearranged design, ensuring maximal efficiency in energy production and the synthesis of essential building blocks for organisms.

Vertebrate Limb Design: Almost every biology textbook contains a drawing of vertebrate limbs, claiming that the similar skeletal structures of the limb of a bat, whale, horse, and human show their common ancestry. But what if there are good functional design reasons for their skeletal similarities that have nothing to do with common ancestry?

Engineer Stuart Burgess published a paper in Bioinspiration & Biomimetics showing that the vertebrate limb structure is reused because it is “highly versatile and optimal not just for arms and legs but also for flippers and wings” and uses “networks of segmented bones that enable smooth morphing of shapes as well as multifunctioning structures” which “fine-tune motions and mechanical advantage.”¹² He recommends humans mimic its structure in our own designs: “The vertebrate limb has significant potential for the bioinspired design of robotic and prosthetic limbs.”

Burgess demonstrated that the limbs of each vertebrate are precisely suited to each animal’s environment and functional needs. For example, birds’ wings are engineered for efficient flight, while the flippers of whales are specialized for aquatic locomotion. In contrast, human forelimbs are optimized for dexterity and precise manipulation. This pattern of highly specialized and efficient limb structures aligns with the expectations of intentional design yet contradicts evolutionary predictions. Evolution, as an unguided process, would be expected to produce suboptimal, makeshift adaptations rather than the finely tuned structures observed in nature.

Systems Engineering Analyses: Arguably the largest ID research project is the “Engineering Research Group,” where biologists and engineers are collaborating to explore how biological systems employ design patterns known from human engineering, including control feedback loops, signal transmission protocols, and just-in-time delivery of materials for manufacturing. The sensory systems in animals implement complex strategies to achieve signal processing, risk management, efficient transmission of energy across interfaces, and adaptation to changing environments.¹³ ID researchers have applied systems engineering modeling tools to biological systems to illustrate their top-down design logic.¹⁴ In short, biological systems were designed according to the same principles employed by systems engineers.

Evolution glibly predicts that biological systems resemble Rube Goldberg devices in which components appear haphazardly linked to inefficiently bungle into some function. In contrast, ID predicts that biological systems should demonstrate the same top-down design seen in human engineering, where a complex system is planned in advance such that multiple sub-systems work together, engineered to optimally achieve the overall purpose. And the latter is precisely what we see in the human body.¹⁵ No undirected process could achieve the high efficiencies seen in life if a mind did not plan everything in advance.

Get Involved!

We’d love to go on telling you about ID research, but we’re out of space. For more information, visit Discovery Institute’s ID 3.0 Home Page at

Discovery.org/id/research/ or see our list of 150+ peer-reviewed pro-ID scientific publications at

Discovery.org/id/peer-review/. Both pages are necessarily incomplete, as some projects and papers cannot be discussed publicly because this could jeopardize the careers of the researchers involved.

Yet even what we can say publicly about ID 3.0 research more than suffices to show that ID not only uses scientific methods but has an active and fruitful scientific research program—guided by a paradigm which says life was intelligently designed. New scientists are joining our research community on a regular basis—to get involved, contact us at Discovery Institute!

Notes
1. Douglas D. Axe, “Extreme Functional Sensitivity to Conservative Amino Acid Changes on Enzyme Exteriors,” Journal of Molecular Biology, 301(3) (2000), 585–95; Douglas D. Axe, “Estimating the Prevalence of Protein Sequences Adopting Functional Enzyme Folds,” Journal of Molecular Biology, 341(5) (2004), 1295–315.
2. Ann K. Gauger and Douglas D. Axe, “The Evolutionary Accessibility of New Enzymes Functions: A Case Study from the Biotin Pathway,” BIO-Complexity (1) (2011), 1–17; Mariclair A. Reeves et al., “Enzyme Families—Shared Evolutionary History or Shared Design? A Study of the GABA-Aminotransferase Family,” BIO-Complexity (4) (2014), 1–16.
3. Lothar Wissler et al., “Mechanisms and Dynamics of Orphan Gene Emergence in Insect Genomes,” Genome Biology and Evolution 5(2) (2013), 439–55.
4. Paul A. Nelson and Richard J. A. Buggs, “Next Generation Apomorphy: The Ubiquity of Taxonomically Restricted Genes,” In: Olson PD, Hughes J, Cotton JA, eds. Next Generation Systematics (2016), 237–263; Richard S. Gunasekera et al., “ORFanID: A Web-Based Search Engine for the Discovery and Identification of Orphan and Taxonomically Restricted Genes,” PLOS One (Oct. 25, 2023).
5. Winston Ewert, “The Dependency Graph of Life,” BIO-Complexity (3) (2018), 1–27.
6. Winston Ewert, “AminoGraph Analysis of the Auditory Protein Prestin From Bats and Whales Reveals a Dependency-Graph Signal That Is Missed by the Standard Convergence Model,” BIO-Complexity (1) (2023), 1–15.
7. Ola Hössjer et al., “On the Waiting Time Until Coordinated Mutations Get Fixed in Regulatory Sequences,” Journal of Theoretical Biology, (524) (2021); Ola Hössjer et al., “Phase-Type Distribution Approximations of the Waiting Time Until Coordinated Mutations Get Fixed in a Population,” In: Silvestrov, S., Malyarenko, A., Rančić, M. (eds) Stochastic Processes and Applications (2017).
8. Richard von Sternberg, “On the Roles of Repetitive DNA Elements in the Context of a Unified Genomic-Epigenetic System,” Annals of the New York Academy of Sciences (981) (Dec. 2022), 154–88.
9. Michael Egnor, “The Cerebral Windkessel as a Dynamic Pulsation Absorber,” BIO-Complexity (3) (2019), 1–35.
10. Michael Egnor et al., “A Quantitative Model of the Cerebral Windkessel and Its Relevance to Disorders of Intracranial Dynamics,” Journal of Neurosurgery, Pediatrics, 32(3) (Jun. 23 2023), 302–311.
11. Gerald L. Fudge and Emily Brown Reeves, “A Model-Based Reverse System Engineering Methodology for Analyzing Complex Biological Systems With a Case Study in Glycolysis,” IEEE Open Journal of Systems Engineering, (2) (2024), 119–134.
12. Stuart Burgess, “Universal Optimal Design in the Vertebrate Limb Pattern and Lessons for Bioinspired Design,” Bioinspiration & Biomimetics, 19 (5) (Aug. 9, 2024).
13. See Science and Faith in Dialogue, Frederik van Niekerk and Nico Vorster (eds) (2022).
14. See Waldean A. Schulz, “An Engineering Perspective on the Bacterial Flagellum: Part 1—Constructive View,” BIO-Complexity (1) (2021); “An Engineering Perspective on the Bacterial Flagellum: Part 2 – Analytic View,” BIO-Complexity (2) (2021); and “An Engineering Perspective on the Bacterial Flagellum: Part 3 – Observations,” BIO-Complexity (3) (2021).
15. See Steve Laufmann and Howard Glicksman, Your Designed Body (2022).